Hand Fracture Detection Using Convolutional Neural Networks: A Deep Learning Approach for Automated Diagnostics

Abstract

This paper presents a Convolutional Neural Network (CNN) model developed for the automated identification of hand fractures from X-ray images, aiming to enhance diagnostic accuracy and efficiency in clinical settings. Hand fractures are common injuries that require prompt and precise diagnosis to prevent complications. Traditional diagnostic methods rely on manual interpretation, which can be time-consuming and prone to human error. Leveraging a dataset of 10,000 annotated hand X-ray images, the proposed CNN model was trained and validated using advanced preprocessing techniques and a carefully designed architecture. The model accomplished a training accuracy of 99.45% and a validation accuracy of 96.68% by the 18th epoch, demonstrating its ability to accurately distinguish between fractured and non-fractured hand images. Additionally, the model achieved a training loss of 0.0127 and a validation loss of 0.1408, indicating strong generalization to new data. These results suggest that the CNN model can significantly improve the speed and reliability of hand fracture diagnosis, offering a valuable tool for clinicians, particularly in resource-limited settings. The model's effectiveness highlights the potential of deep learning in medical image analysis and paves the way for its integration into clinical workflows as a decision support system.

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